ddl.tree.TreeDestructor

class ddl.tree.TreeDestructor(tree_density=None)[source]

Bases: ddl.base.BaseDensityDestructor

Canonical tree destructor based on an underlying tree density.

This canonical destructor assumes that the underlying density is a tree density ( i.e. TreeDensity). The destructor is canonical because the tree density is defined on the unit hypercube and thus the domain of this destructor is also the unit hypercube. See TreeDensity for information on possible estimator parameters.

Parameters:
tree_density : TreeDensity

The tree density estimator for this destructor.
Attributes:
density_ : TreeDensity

Fitted underlying tree density.

See also

TreeDensity

Methods

fit(self, X[, y, density_fit_params]) [Placeholder].
fit_from_density(self, density) [Placeholder].
fit_transform(self, X[, y]) Fit to data, then transform it.
get_domain(self) Get the domain of this destructor.
get_params(self[, deep]) Get parameters for this estimator.
inverse_transform(self, X[, y]) Apply inverse destructive transformation to X.
sample(self[, n_samples, random_state]) Generate random samples from this density/destructor.
score(self, X[, y]) Return the mean log likelihood (or log(det(Jacobian))).
score_samples(self, X[, y]) Compute log-likelihood (or log(det(Jacobian))) for each sample.
set_params(self, \*\*params) Set the parameters of this estimator.
transform(self, X[, y]) Apply destructive transformation to X.
create_fitted  
__init__(self, tree_density=None)[source]

Initialize self. See help(type(self)) for accurate signature.

classmethod create_fitted(fitted_density, **kwargs)[source]
fit(self, X, y=None, density_fit_params=None)

[Placeholder].

Parameters:
X :
y :
density_fit_params :
Returns:
obj : object
fit_from_density(self, density)

[Placeholder].

Parameters:
density :
Returns:
obj : object
fit_transform(self, X, y=None, **fit_params)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:
X : numpy array of shape [n_samples, n_features]

Training set.

y : numpy array of shape [n_samples]

Target values.
Returns:
X_new : numpy array of shape [n_samples, n_features_new]

Transformed array.
get_domain(self)

Get the domain of this destructor.

Returns:
domain : array-like, shape (2,) or shape (n_features, 2)

If shape is (2, ), then domain[0] is the minimum and domain[1] is the maximum for all features. If shape is (n_features, 2), then each feature’s domain (which could be different for each feature) is given similar to the first case.
get_params(self, deep=True)

Get parameters for this estimator.

Parameters:
deep : boolean, optional

If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:
params : mapping of string to any

Parameter names mapped to their values.
inverse_transform(self, X, y=None)[source]

Apply inverse destructive transformation to X.

Parameters:
X : array-like, shape (n_samples, n_features)

New data, where n_samples is the number of samples and n_features is the number of features.

y : None, default=None

Not used in the transformation but kept for compatibility.
Returns:
X_new : array-like, shape (n_samples, n_features)

Transformed data.
sample(self, n_samples=1, random_state=None)

Generate random samples from this density/destructor.

Parameters:
n_samples : int, default=1

Number of samples to generate. Defaults to 1.

random_state : int, RandomState instance or None, optional (default=None)

If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by numpy.random.
Returns:
X : array, shape (n_samples, n_features)

Randomly generated sample.
score(self, X, y=None)

Return the mean log likelihood (or log(det(Jacobian))).

Parameters:
X : array-like, shape (n_samples, n_features)

New data, where n_samples is the number of samples and n_features is the number of features.

y : None, default=None

Not used but kept for compatibility.
Returns:
log_likelihood : float

Mean log likelihood data points in X.
score_samples(self, X, y=None)

Compute log-likelihood (or log(det(Jacobian))) for each sample.

Parameters:
X : array-like, shape (n_samples, n_features)

New data, where n_samples is the number of samples and n_features is the number of features.

y : None, default=None

Not used but kept for compatibility.
Returns:
log_likelihood : array, shape (n_samples,)

Log likelihood of each data point in X.
set_params(self, **params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns:
self
transform(self, X, y=None)[source]

Apply destructive transformation to X.

Parameters:
X : array-like, shape (n_samples, n_features)

New data, where n_samples is the number of samples and n_features is the number of features.

y : None, default=None

Not used in the transformation but kept for compatibility.
Returns:
X_new : array-like, shape (n_samples, n_features)

Transformed data.